Estimation of expected value for remaining work time for contact center agents

ABSTRACT

The present invention provides a more accurate estimate as to time for completion of a call by using estimated time durations of separate phases of the call and by determining what phase a call is currently on. An important feature of the present invention is the use of automated speech processing techniques to estimate where the customer and agent are in their conversation and to gauge the rate of progress of the call.

FIELD OF THE INVENTION

The present invention relates generally to call centers of other callprocessing systems in which calls are distributed among a numbers ofagents for handling.

BACKGROUND OF THE INVENTION

In a typical inbound contact center arrangement, customer servicerepresentatives handle a variety of incoming calls. For each new callthat a single or multi-site set of call centers receives, decisions aremade by call routing algorithms as to which site should handle a call,and then within the site, whether a call should be given to an availableagent, or held in queue for an agent that is more likely to have theskills required to handle the call. An example of a prior art systemwhich utilizes such algorithms is the Advocate System, marketed by AvayaInc. of Basking Ridge, N.J.

In a typical outbound contact center, an effort is made to optimizeperformance of the agents by placing predictive dialing calls. Apredictive dialing call is one that is placed in anticipation of anagent becoming free in the very near future. Should this prediction beincorrect, the called party answers the phone, hears no one on the otherend, and hangs up. A potential customer, so treated, would be even lessreceptive to a subsequent call from an agent. For an outbound contactcenter, the decision of when to place a predictive dialing call istypically based on the expected remaining work time of all agents thatmight be able to handle the call about to be placed. It is in the bestinterests of both the outbound call center and the called party thatthis decision is made as accurately as possible.

One of the inputs to such predictive algorithms, either directly or viacalculation, is the expected remaining work time of each agent. Today,this estimate is based on the average of all calls handled by the agentor by all agents. If the general type of call is known (due to theoriginal number dialed by the customer, or selections made for routingusing an interactive voice response (IVR) system, or information held inthe customer record), the average can be computed for calls of a certaintype. Considering types of calls in this manner improves the accuracy ofthe estimate by reducing the variance of the estimate.

Accordingly, in a typical current prior art systems, estimated remainingwork time is calculated by determining the average call holding time forall calls of a class, and then subtracting the time an agent has spenton the call up to this point. It is well-known in the art that whilesuch estimates can be fairly accurate when it entails a large callvolume, as call volume decreases (e.g., based on time of day or day ofweek), predictors based on such estimates become far less accurate. Inmany instances, the actual length of the call will exceed the averagecall holding time, and thus the estimate will be zero or negative.

In all the above cases, the estimated remaining work time for aparticular call to an agent is only a general estimate and does not takeinto account the current pace of the call in progress with the agent. Acommonly owned, co-pending application, U.S. patent application Ser. No.09/675,729 filed Sep. 29, 2000 and incorporated herein by reference,utilizes the fact that a call between an agent and a customer passesthrough distinct phases. By estimating time durations of these separatephases and by determining what phase a call is currently on, a moreaccurate estimate is derived as to time for completion of a call.

The present invention improves upon the prior art estimation techniquesabove by using additional information to estimate where the customer andagent are in their conversation on the call. With this information, amore accurate estimate can be determined of the remaining time of thecall. This estimate is then provided well known routing algorithms toimprove the results of the routing function.

SUMMARY OF THE INVENTION

The present invention provides a more accurate estimate as to time forcompletion of a call by using estimated time durations of separatephases of the call and by determining what phase a call is currently on.An important feature of the present invention is the use of automatedspeech processing techniques to estimate where the customer and agentare in their conversation and to gauge the rate of progress of the call.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present invention will now be described indetail in conjunction with the annexed drawings, in which:

FIG. 1 is a block diagram of a call center in which one or more aspectsof the present invention may be implemented;

FIG. 2 is a block diagram of an automatic call distribution (ACD) systemimplemented in the call center of FIG. 1.

DETAILED DESCRIPTION

Although the invention will be illustrated below in conjunction with theprocessing of calls in an exemplary call center, it is not limited touse with any particular type of call center or communication processingapplication. For example, the invention is applicable to the processingof incoming communications, outgoing communications or both. Thedisclosed techniques can be used with automatic call distribution (ACD)systems, telemarketing systems, private-branch exchange (PBX) systems,computer-telephony integration (CTI)-based systems, as well as incombinations of these and other types of call centers. A call center inaccordance with the invention may be configured using any type ofnetwork infrastructure, such as, e.g., asynchronous transfer mode (ATM),local area networks, wide area networks, Internet Protocol (IP)networks, etc. The term “call center” as used herein is thus intended toinclude any type of ACD system, telemarketing system or othercommunication system which processes calls or other service requests.The term “call” as used herein is intended to include any of theabove-noted types of communications as well as portions or combinationsof these and other communications.

FIG. 1 shows an illustrative call center in which the present inventionmay be implemented. The call center includes a number of telephone linesand/or trunks 100 selectively interconnected with a plurality of agentpositions 102-104 via an ACD system 101. Each agent position 102-104includes a voice-and-data terminal 105 for use by a corresponding agent106-108 in handling calls. The terminals 105 are connected to ACD system101 by a voice-and-data transmission medium 109. The ACD system 101includes a conventional basic call management system (BCMS) 110, and isalso connected to a conventional external call management system (CMS)111. The BCMS 110 and CMS 111 gather call records, call centerstatistics and other information for use in managing the call center,generating call center reports, and performing other functions. Inalternative embodiments, the functions of the BCMS 110 and the CMS 111may be provided using a single call management system internal orexternal to the ACD system 101.

The ACD system 101 may be implemented in a manner similar to, forexample, the Definity.RTM. PBX-based ACD system from LucentTechnologies. FIG. 2 shows a simplified block diagram of one possibleimplementation of ACD system 101. The system 101 as shown in FIG. 2 is astored-program-controlled system that includes interfaces 112 toexternal communication links, a communications switching fabric 113,service circuits 114 (e.g., tone generators, announcement circuits,etc.), a memory 115 for storing control programs and data, and aprocessor 116 (e.g., a microprocessor, a CPU, a computer, etc. orvarious portions or combinations thereof) for executing the storedcontrol programs to control the interfaces and the fabric, to provideautomatic call distribution functionality, and to provide storage ofe-mails, faxes and other communications.

Referring again to FIG. 1, exemplary data elements stored in the memory115 of ACD system 101 include a set of call queues 120 and a set ofagent queues 130. Each call queue 121-129 in the set of call queues 120corresponds to a different agent skill, as does each agent queue 131-139in the set of agent queues 130. As in a conventional system, calls areprioritized, and may be, for example, enqueued in individual ones of thecall queues 120 in their order of priority, or enqueued in differentones of a plurality of call queues that correspond to a skill and eachone of which corresponds to a different priority. Similarly, eachagent's skills are prioritized according to his or her level ofexpertise in that skill, and agents may be, for example, enqueued inindividual ones of the agent queues 130 in their order of expertiselevel, or enqueued in different ones of a plurality of agent queues thatcorrespond to a skill and each one of which corresponds to a differentexpertise level in that skill. It should be noted that the invention canalso be implemented in systems using a wide variety of other types ofqueue arrangements and queuing techniques.

The ACD system 101 further includes a call vector 140. The call vector140 may be one of a number of different types of stored control programsimplemented in system 101. Calls incoming to the call center on lines ortrunks 100 are assigned by call vector 140 to different call queues121-129 based upon the agent skill that they require for properhandling. Agents 106-108 who are available for handling calls areassigned to agent queues 131-139 based upon the skills which theypossess. An agent may have multiple skills, and hence may be assigned tomultiple agent queues 131-139 simultaneously. Such an agent is referredto herein as a “multi-skill agent.” Furthermore, an agent may havedifferent levels of skill expertise (e.g., different skill levels in amulti-level scale or primary (P) and secondary (S) skills), and hencemay be assigned to different agent queues 131-139 at different expertiselevels.

Call vectoring is described in greater detail in Definity.RTM.Communications System Generic 3 Call Vectoring/Expert Agent Selection(EAS) Guide, AT&T Publication No. 555-230-520, Issue 3, November 1993,which is incorporated by reference herein. Skills-based ACD techniquesare described in greater detail in, for example, U.S. Pat. No. 5,206,903issued Apr. 27, 1993 in the name of inventors J. E. Kohler et al. andentitled “Automatic Call Distribution Based on Matching Required Skillswith Agents Skills,” which is incorporated by reference herein.

Another program executing in ACD system 101 is an agent selector 150.Selector 150 may be implemented in software stored either in the memory115 of system 101, in a peripheral memory (e.g., a disk, CD-ROM, etc.)of system 101, or in any other type of computer readable mediumassociated with system 101, and executed by processor 116 or othersuitable processing hardware associated with the ACD system 101.Selector 150 in this exemplary embodiment implements conventionaltechniques for providing an assignment between available calls andavailable agents. The conventional techniques implemented by selector150 are well known in the art and will not be further described herein.It should be noted that these functions could be implemented in otherelements of the ACD system 101, or using a combination of a number ofdifferent elements in such a system.

Further details regarding call processing in a system such as ACD system101 can be found in, for example, U.S. Pat. No. 5,905,793 issued May 18,1999 in the name of inventors A. D. Flockhart et al. and entitled“Waiting-Call Selection Based on Anticipated Wait Times,” and U.S. Pat.No. 6,192,122 issued Feb. 20, 2001 in the name of inventors A. D.Flockhart et al. and entitled “Call Center Agent Selection thatOptimizes Call Wait Times,” both of which are incorporated by referenceherein.

In accordance with the present invention, the call center of FIG. 1 isconfigured to include capabilities for implementing the use ofpredictive algorithms which make use of the well-known fact that a callbetween an agent and a customer passes through distinct phases.

In one embodiment of the invention an estimate is determined of theprobability that the call is currently in a given phase. Then, usinghistorical data from the call center on length of time spent in eachphase, the invention estimates the most likely time remaining that thecurrent caller will remain in that current phase. Finally, by modelingthe transitions from phase to phase to call termination, and summing theexpected time that will be spent in each phase yet to be encounteredduring the call, the invention then provides an estimate of theremaining holding time that will be spent in each phase yet to beencountered during the call. In this manner, the invention obtains anestimate of the remaining holding time for the call.

Typically in prior art processing of call, an agent relies on an imageon his computer screen to provide a “talking points” script and/or atemplate for gathering data. These screens are therefore usuallyencountered in a specific order. In the commonly owned, copending U.S.patent application Ser. No. 09/675,729 referenced above in the prior artdiscussion, a determination is made of which screen an agent hadrecently viewed, and to estimate average time to end of call based uponthis input. The current invention identifies phases of a call, and thenestimates the current phase the agent is in by reviewing a plurality ofinputs. That is, the current invention utilizes more than recent screensviewed. In particular, it also considers automated speech processingresults and other multimodal inputs such as a selection on a PersonalDigital Assistant (PDA) during a voice/PDA call. Such automated speechprocessing techniques include Automatic Speech Recognition (ASR) whereinthe presence of specific spoken words is detected. Further embodimentsof the invention include additional well-known speech processingtechniques which detect a speaker's accent, disfluency rate, speakingrate and other features of speech that have a potential impact on theprogress of a call. That is, a speaker's speaking rate will directlyeffect the rate of information transfer and subsequently, the expectedtime of completion of the call. Similarly, an accented and/or disfluentspeaker (e.g., speaker that stutters, restarts phrases, hesitates, usesfiller words or phrases, etc.) will frequently take longer tocommunicate, adversely effecting the progress of the call. Still furtherembodiments of the invention would evaluate the proportion of timeduring the call or phase in which the customer is talking versus theagent talking. This proportion is indicative of a customer who is more(or less) verbose than normal thereby influencing the progress of thecall. These additional embodiments of the invention take account one ormore of these additional factors in determining the expected time ofcall completion.

Various embodiments of invention detect and estimate what phase a callis in at any given time. It is well known in the art that not every callwill pass through all possible phases, and that some calls may evenrepeat phases (e.g., in a help desk situation, the caller may be askedto repeat their problem after an initial proposed solution has failed).In various embodiments of the invention the following mechanisms areemployed:

-   -   1. Use of speech recognition to listen to the agent and/or the        customer to detect the general topic of their current        discussion. Use of various well-known prior art techniques        (e.g., a combination of text analysis techniques such as neural        networks, Latent Semantic Indexing, gisting, sequence package        analysis, or word spotting) can then be used to estimate the        probability that the conversation is in each possible phase for        a short portion of the prior discussion heard. Additional        embodiments of the invention then use this probability vector as        input into a digital filtering process to estimate the current        phase. As noted above, additional inputs to this filtering        process can be parameters determined by various automated speech        processing techniques (e.g., accent detection, speaking rate        etc.) that effect the rate of progress of the call.    -   2. Agents commonly access a set of screens or forms to guide        their interaction with the customer, and to input or retrieve        information from a centralized business system database. The        mere fact that a screen is displayed on the agent's workstation        can identify the current phase of the call, or can be used as an        input parameter to a digital filtering process or a Hidden        Markov Model (HMM) in the same way that the speech        technology-based estimate of call phase could be used as input        into the HMM-based phase identification model. This method can        thus derive estimated duration to end of call from the HMM even        if the agent's use of the screens does not singularly indicate.    -   3. Use of the act of filling in the content of selected fields        on the screen, along with the screen itself as input vectors to        phase estimation model.    -   4. Use of a combination of the output of speech recognition        module and of the agent interaction module can be used as input        to a digital filtering process for phase state estimation.

Additional embodiments of the invention employ methods that model theflow from phase to phase within a call. These methods include:

-   -   1. An algorithm that identifies only the phases that occur        immediately before most calls terminate. Then, when the call is        not in one of these near-the-end phases, one can determine an        estimate of the Remaining Expected Wait Time (REWT) as the        difference of the Average Wait Time (AWT) and the call duration.        That is,        REWT=AWT−(call duration).    -   When the call enters a near-the-end phase, a more accurate value        is calculated for REWT as being the difference of the AWT for        the phase and the call duration within the phase. That is,        REWT =(AWT-for-the-phase)−(call duration within the phase).    -   2. An algorithm to model the transition from phases as a HMM.        The input vector to the model would be the output of the phase        estimation algorithm, and the duration that the call has spent        in that phase up to this point in time. Additional embodiments        of the invention contemplate use of more complex feature        components, to include, but not be limited to, the ratio of time        this caller has spent in phases up to this point in time to the        average time that all callers spend in phases up to this point        in the call (to capture the “pace” of the call relative to all        calls). Using the distribution of expected time within each        phase, and the probability of transition between phases are used        to calculate expected remaining work time for the call.

One embodiment of the present invention can be readily implemented onmany existing prior art call centers by employing desktop wideband ASRto listen to the agent and then using automatic techniques to build acall classification model with two states:

-   -   1. Not within N seconds of end of call, or    -   2. Within N seconds of end of call.

Various additional embodiments of the invention permit the system, overa period of time, to learn automatically from the interaction of anagent with customers, to determine when it has a good model of the callflow, and then to begin analyzing calls form that point forward usingcall classification techniques to classify agents' utterances as “nearthe end” or not. Additional embodiments of the invention would identifyadditional states (e.g., end of call greater than 60 seconds away, endof call between 60 and 30 seconds, or end of call 30 seconds or less) tofurther improve the accuracy of the estimation process.

It will be understood that the forgoing description of the invention isby way of example only, and variations will be evident to those skilledin the art without departing from the scope of the invention, which isas set out in the appended claims.

1. A call management system for interconnecting a customer who is usinga communication device, with one of a plurality of customer agents; saidinterconnection thereby establishing a service call, said callmanagement system comprising: means for segmenting said call into aplurality of phases; means for predicting a current phase of said callfrom said plurality of phases; and, means for estimating time remainingon said call.
 2. The call management system of claim 1 wherein saidmeans for estimating comprises a means for classifying said call intoone of a plurality of call classes.
 3. The call management system ofclaim 2 wherein said means for estimating further comprises means forperforming methods of automatic speech analysis upon the service call.4. The call management system of claim 3 wherein said methods ofautomatic speech analysis are selected from the group consisting ofAutomatic Speech Recognition, accent recognition, disfluencyrecognition, speaking rate categorization, and verbosity categorization.5. The call management system of claim 4 further comprising: means forqueuing additional calls awaiting an available agent from said pluralityof customer agents; means for predicting the availability of an agentcurrently engaged in a service call based on said estimated timeremaining on said call and, means for assigning one of said queuedadditional calls to said currently engaged agent.
 6. The call managementsystem of claim 5, wherein the call management system is an outboundcontact center and wherein the system further comprises: means fororiginating an outbound call to a customer prior to the currentlyengaged agent completing the service call.
 7. The call management systemof claim 3 wherein said means for estimating further comprisesevaluating the proportion of time the customer speaks relative to timethe agent speaks.
 8. The call management system of claim 3 wherein saidmeans for estimating further comprises evaluating status of a computerscreen displayed to the agent.
 9. The call management system of claim 3wherein said means for estimating further comprises means for modelingthe flow from one phase of said plurality of phases of the call toanother phase of said plurality of phases of the call.
 10. The callmanagement system of claim 9, further comprising a feedback means forimproving accuracy of said modeling means by utilizing feedback of whenthe call actually ended.
 11. A method estimating the time remaining on aservice call, for use in a call management system which interconnects acustomer who is using a communication device, with one of a plurality ofcustomer agents; said interconnection thereby establishing said servicecall; said method comprising the steps of: segmenting said call into aplurality of phases; predicting a current phase of the call from saidplurality of phases; and, estimating time remaining on said call usingsaid predicted current phase.
 12. The method of claim 11 wherein saidestimating step comprises a step of classifying said call into one of aplurality of call classes.
 13. The method of claim 12 wherein saidestimating step further comprises a step of performing methods ofautomatic speech analysis upon the service call.
 14. The method of claim13 wherein said methods of automatic speech analysis are selected fromthe group consisting of Automatic Speech Recognition, accentrecognition, disfluency recognition, speaking rate categorization, andverbosity categorization.
 15. The method of claim 13 wherein saidestimating step further comprises a step of evaluating the proportion oftime the customer speaks relative to time the agent speaks.
 16. Themethod of claim 13 wherein said estimating step further comprises a stepof evaluating status of a computer screen displayed to the agent. 17.The method of claim 13 wherein said estimating step further comprises astep of modeling the flow from one phase of said plurality of phases ofthe call to another phase of said plurality of phases of the call. 18.The method of claim 17 further comprising the step of improving accuracyof said modeling step by providing feedback of when the call actuallyended.